Analysis Of Texture Features For Wood Defect Classification

Selecting important features in classifying wood defects remains a challenging issue to the automated visual inspection domain. This study aims to address the extraction and analysis of features based on statistical texture on images of wood defects. A series of procedures including feature extraction using the Grey Level Dependence Matrix (GLDM) and feature analysis were executed in order to investigate the appropriate displacement and quantisation parameters that could significantly classify wood defects. Samples were taken from the Kembang Semangkuk (KSK), Meranti and Merbau wood species. Findings from visual analysis and classification accuracy measures suggest that the feature set with the displacement parameter, d=2, and quantisation level, q=128, shows the highest classification accuracy. However, to achieve less computational cost, the feature set with quantisation level, q=32, shows acceptable performance in terms of classification accurac

Systematic Feature Analysis On Timber Defect Images

Feature extraction is unquestionably an important process in a pattern recognition system.A clearly defined set of features makes the identification task more effective. This paper addresses the extraction and analysis of features based on statistical texture to characterize images of timber defects.A series of procedures including feature extraction and feature analysis was executed in order to construct an appropriate feature set that could significantly distinguish amongst defects and clear wood classes.The feature set is aimed for later use in a timber defect detection system.To assess the discrimination capability of the features extracted, visual exploratory analysis and statistical confirmatory analysis were performed on defect and clear wood images of Meranti (Shorea spp.)timber species.Findings from the analysis demonstrated that utilizing the proposed set of texture features resulted in significant distinction between defect classes and clear wood

Systematic feature analysis on timber defect images

Feature extraction is unquestionably an important process in a pattern recognition system. A defined set of features makes the identification task more efficiently. This paper addresses the extraction and analysis of features based on statistical texture to characterize images of timber defects. A series of procedures including feature extraction and feature analysis was executed to construct an appropriate feature set that could significantly separate amongst defects and clear wood classes. The feature set aimed for later use in a timber defect detection system. For Accessing the discrimination capability of the features extracted, visual exploratory analysis and confirmatory statistical analysis were performed on defect and clear wood images of Meranti (Shorea spp.) timber species. Results from the analysis demonstrated that there was a significant distinction between defect classes and clear wood utilizing the proposed set of texture features

Fundamental Review on the Formulation of Large Lattice Spatial Neighbor Matrices

To calculate the impact of each location within an observation area we need to calculate the dependences of that location. In order to specify the model to explain this condition, we must define the neighbor relation for each location. This important information is described by a spatial neighbor matrix (Cressie, 1991: Ch. 6). By using Spatial Matrix Dr×c, which is extracted from polygon structure of spatial lattice DM, we can construct Neighbor Relation Matrix, W. There should be several methods to construct W matrix, such as: 1) Direct Arrow Reading (DAR); 2) Inner-Outer Neighbor Matrix (ION); and 3) Kronecker Product.In this research, we verified the algorithm performances based on their time and space efficiency. All of them were calculated based on the complexity and real execution. We found that Kronecker product method became the best method to construct W matrix. That method can be used efficiently both in terms of computational time and space

An efficient robust hyperheuristic clustering algorithm

Observations on recent research of clustering problems illustrate that most of the approaches used to deal with these problems are based on meta-heuristic and hybrid meta-heuristic to improve the solutions. Hyperheuristic is a set of heuristics, meta- heuristics and high-level search strategies that work on the heuristic search space instead of solution search space. Hyperheuristics techniques have been employed to develop approaches that are more general than optimization search methods and traditional techniques. In the last few years, most studies have focused considerably on the hyperheuristic algorithms to find generalized solutions but highly required robust and efficient solutions. The main idea in this research is to develop techniques that are able to provide an appropriate level of efficiency and high performance to find a class of basic level heuristic over different type of combinatorial optimization problems. Clustering is an unsupervised method in the data mining and pattern recognition. Nevertheless, most of the clustering algorithms are unstable and very sensitive to their input parameters. This study, proposes an efficient and robust hyperheuristic clustering algorithm to find approximate solutions and attempts to generalize the algorithm for different cluster problem domains. Our proposed clustering algorithm has managed to minimize the dissimilarity of all points of a cluster using hyperheuristic method, from the gravity center of the cluster with respect to capacity constraints in each cluster. The algorithm of hyperheuristic has emerged from pool of heuristic techniques. Mapping between solution spaces is one of the powerful and prevalent techniques in optimization domains. Most of the existing algorithms work directly with solution spaces where in some cases is very difficult and is sometime impossible due to the dynamic behavior of data and algorithm. By mapping the heuristic space into solution spaces, it would be possible to make easy decision to solve clustering problems. The proposed hyperheuristic clustering algorithm performs four major components including selection, decision, admission and hybrid metaheuristic algorithm. The intensive experiments have proven that the proposed algorithm has successfully produced robust and efficient clustering results

Rotation Invariant Texture Feature Based on Spatial Dependence Matrix for Timber Defect Detection

This paper addresses the issue of extracting textural feature for timber defect detection. Statistical features based on spatial dependence matrix are extracted for both classes; clear wood and defect. Instead of using the classical directional matrices, rotation invariant spatial dependence matrix formulation is applied to ensure accurate detection regardless of the timber feed direction. Hotelling T-Squared test is used to measure significance difference of mean between feature distributions of the two classes. The result will give some indication to whether the features extracted are sufficient/good enough to be used in future classification stage